modify readme

README.md

@@ -24,6 +24,39 @@ task_ids:
 
 This is the repository for PLOD Dataset subset being used for CW in NLP module 2023-2024 at University of Surrey. 
 
+### Dataset Summary
+
+This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain. 
+
+### Supported Tasks and Leaderboards
+
+This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.
+
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`.
+
+An example from the dataset:
+{'id': '1',
+ 'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],
+ 'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],
+ 'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+}
+
+### Data Fields
+
+- id: the row identifier for the dataset point.
+- tokens: The tokens contained in the text.
+- pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.
+- ner_tags: The tags for abbreviations and long-forms.
+
 ### Original Dataset (Only for exploration. For CW, You must USE THE PLOD-CW subset)
 
 We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.
@@ -67,38 +100,6 @@ We provide two variants of our dataset - Filtered and Unfiltered. They are descr
 - **Leaderboard:** https://paperswithcode.com/sota/abbreviationdetection-on-plod-filtered
 - **Point of Contact:** [Diptesh Kanojia](mailto:d.kanojia@surrey.ac.uk)
 
-### Dataset Summary
-
-This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain. 
-
-### Supported Tasks and Leaderboards
-
-This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.
-
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`.
-
-An example from the dataset:
-{'id': '1',
- 'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],
- 'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],
- 'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-}
-
-### Data Fields
-
-- id: the row identifier for the dataset point.
-- tokens: The tokens contained in the text.
-- pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.
-- ner_tags: The tags for abbreviations and long-forms.
 
 ## Dataset Creation